{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# EDA"
      ],
      "metadata": {
        "id": "Nc0HwZQYT88Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install Required Libraries"
      ],
      "metadata": {
        "id": "N76xNLe6T7dE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Title: Install Visualization & Modeling Libraries\n",
        "!pip install -q seaborn autogluon\n",
        "\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVU1tWExTxNV",
        "outputId": "6bbf22a7-56a5-45ef-9aba-c6974c87adc7"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.0/44.0 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m259.5/259.5 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.2/87.2 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.3/62.3 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.1/6.1 MB\u001b[0m \u001b[31m75.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.5/201.5 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.2/128.2 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m395.9/395.9 kB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m247.0/247.0 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m469.0/469.0 kB\u001b[0m \u001b[31m27.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.3/135.3 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.3/55.3 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m78.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for nvidia-ml-py3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the Dataset"
      ],
      "metadata": {
        "id": "h8ZQY9UrT_Pe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/UCI_Syncora_Synthetic.csv')\n",
        "df.drop(columns=['Unnamed: 0'], inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "GpODwgrwTxjY",
        "outputId": "8514e01e-c2dc-448e-f6e4-a19dea479c2d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
              "0   44893.650314    1          2         2   40      0      0      0      0   \n",
              "1   18424.519098    1          2         2   21      0      0      2      2   \n",
              "2  117876.942766    1          3         2   50      0      0      0      2   \n",
              "3   54723.377935    2          2         2   30      0      0      0      0   \n",
              "4   76558.094259    2          3         1   54      0      0      0      0   \n",
              "\n",
              "   PAY_5  ...      BILL_AMT4      BILL_AMT5      BILL_AMT6     PAY_AMT1  \\\n",
              "0      0  ...   39360.399439   41649.500807   45785.835974   676.382844   \n",
              "1      2  ...   26782.295347   19924.719625   21855.940973  3242.199560   \n",
              "2      0  ...  116157.189638  119962.735807  120028.667538  5274.265065   \n",
              "3      0  ...   38065.930175   41540.058630   41878.685884  3270.158117   \n",
              "4      0  ...   45179.788459   44981.277410   45357.868242  4672.139378   \n",
              "\n",
              "      PAY_AMT2     PAY_AMT3     PAY_AMT4     PAY_AMT5     PAY_AMT6  \\\n",
              "0  2449.199711  2449.919030   680.223794  1615.226897  2121.893044   \n",
              "1   -41.876810  2750.676186   527.399083  1308.591377   514.357090   \n",
              "2  9610.239801  1575.956876  4876.071652  5475.820671  5109.827970   \n",
              "3  4596.396245  1604.847688  1427.425628  1673.040070  3355.872775   \n",
              "4  4242.405416  5329.088732  4598.029179  6111.203016  4883.652725   \n",
              "\n",
              "   default.payment.next.month  \n",
              "0                           0  \n",
              "1                           0  \n",
              "2                           0  \n",
              "3                           0  \n",
              "4                           0  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-a0d349db-bae5-4d5a-add2-736c82415147 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Basic Overview & Data Integrity"
      ],
      "metadata": {
        "id": "XQWiSoDGUCbe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(\"Shape:\", df.shape)\n",
        "print(\"\\nMissing values:\\n\", df.isnull().sum())\n",
        "df.describe().T\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "b5pqNEXfT4ZX",
        "outputId": "9a6ac347-6d28-4935-d216-568a6145cf16"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (49999, 24)\n",
            "\n",
            "Missing values:\n",
            " LIMIT_BAL                     0\n",
            "SEX                           0\n",
            "EDUCATION                     0\n",
            "MARRIAGE                      0\n",
            "AGE                           0\n",
            "PAY_0                         0\n",
            "PAY_2                         0\n",
            "PAY_3                         0\n",
            "PAY_4                         0\n",
            "PAY_5                         0\n",
            "PAY_6                         0\n",
            "BILL_AMT1                     0\n",
            "BILL_AMT2                     0\n",
            "BILL_AMT3                     0\n",
            "BILL_AMT4                     0\n",
            "BILL_AMT5                     0\n",
            "BILL_AMT6                     0\n",
            "PAY_AMT1                      0\n",
            "PAY_AMT2                      0\n",
            "PAY_AMT3                      0\n",
            "PAY_AMT4                      0\n",
            "PAY_AMT5                      0\n",
            "PAY_AMT6                      0\n",
            "default.payment.next.month    0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              count           mean            std  \\\n",
              "LIMIT_BAL                   49999.0  167126.662762  129389.679875   \n",
              "SEX                         49999.0       1.597532       0.490400   \n",
              "EDUCATION                   49999.0       1.856117       0.795364   \n",
              "MARRIAGE                    49999.0       1.556391       0.520025   \n",
              "AGE                         49999.0      34.913598       9.213024   \n",
              "PAY_0                       49999.0      -0.010840       1.121737   \n",
              "PAY_2                       49999.0      -0.134203       1.196782   \n",
              "PAY_3                       49999.0      -0.163703       1.195190   \n",
              "PAY_4                       49999.0      -0.223964       1.153696   \n",
              "PAY_5                       49999.0      -0.272605       1.118507   \n",
              "PAY_6                       49999.0      -0.291966       1.136455   \n",
              "BILL_AMT1                   49999.0   51120.824255   74036.985755   \n",
              "BILL_AMT2                   49999.0   49160.619566   71578.361608   \n",
              "BILL_AMT3                   49999.0   47086.858939   69229.258620   \n",
              "BILL_AMT4                   49999.0   43350.776213   64852.723120   \n",
              "BILL_AMT5                   49999.0   40357.187166   60887.223735   \n",
              "BILL_AMT6                   49999.0   38990.324240   59592.733877   \n",
              "PAY_AMT1                    49999.0    5681.427723   15529.563304   \n",
              "PAY_AMT2                    49999.0    5884.799531   20210.866543   \n",
              "PAY_AMT3                    49999.0    5315.574885   17667.839286   \n",
              "PAY_AMT4                    49999.0    4923.458746   16061.663860   \n",
              "PAY_AMT5                    49999.0    4866.373531   15400.026636   \n",
              "PAY_AMT6                    49999.0    5260.429843   18180.141450   \n",
              "default.payment.next.month  49999.0       0.218204       0.413031   \n",
              "\n",
              "                                      min           25%            50%  \\\n",
              "LIMIT_BAL                     -401.439185  55279.994116  137605.335989   \n",
              "SEX                              1.000000      1.000000       2.000000   \n",
              "EDUCATION                        0.000000      1.000000       2.000000   \n",
              "MARRIAGE                         0.000000      1.000000       2.000000   \n",
              "AGE                             18.000000     28.000000      33.000000   \n",
              "PAY_0                           -2.000000     -1.000000       0.000000   \n",
              "PAY_2                           -3.000000     -1.000000       0.000000   \n",
              "PAY_3                           -3.000000     -1.000000       0.000000   \n",
              "PAY_4                           -3.000000     -1.000000       0.000000   \n",
              "PAY_5                           -3.000000     -1.000000       0.000000   \n",
              "PAY_6                           -2.000000     -1.000000       0.000000   \n",
              "BILL_AMT1                   -34788.432550   5736.131465   23415.854687   \n",
              "BILL_AMT2                   -47703.143243   5208.137901   22541.778530   \n",
              "BILL_AMT3                  -153884.093383   4865.387200   21484.982297   \n",
              "BILL_AMT4                  -192387.606577   4360.350182   19609.362333   \n",
              "BILL_AMT5                   -77988.204777   3754.384863   18258.953502   \n",
              "BILL_AMT6                  -233709.690512   3298.620791   17445.779665   \n",
              "PAY_AMT1                     -8614.726686    816.994345    2433.939769   \n",
              "PAY_AMT2                    -13809.309671    584.506525    2426.046817   \n",
              "PAY_AMT3                     -8212.163893    464.847943    1951.106039   \n",
              "PAY_AMT4                     -7785.048513    373.986983    1705.945428   \n",
              "PAY_AMT5                     -7680.862564    370.304458    1752.391968   \n",
              "PAY_AMT6                     -8498.549728    317.505486    1715.574673   \n",
              "default.payment.next.month       0.000000      0.000000       0.000000   \n",
              "\n",
              "                                      75%           max  \n",
              "LIMIT_BAL                   238648.339988  9.935403e+05  \n",
              "SEX                              2.000000  2.000000e+00  \n",
              "EDUCATION                        2.000000  6.000000e+00  \n",
              "MARRIAGE                         2.000000  3.000000e+00  \n",
              "AGE                             41.000000  7.900000e+01  \n",
              "PAY_0                            0.000000  8.000000e+00  \n",
              "PAY_2                            0.000000  8.000000e+00  \n",
              "PAY_3                            0.000000  8.000000e+00  \n",
              "PAY_4                            0.000000  8.000000e+00  \n",
              "PAY_5                            0.000000  7.000000e+00  \n",
              "PAY_6                            0.000000  8.000000e+00  \n",
              "BILL_AMT1                    66601.246049  9.603367e+05  \n",
              "BILL_AMT2                    64243.516380  9.829584e+05  \n",
              "BILL_AMT3                    60808.080841  8.570844e+05  \n",
              "BILL_AMT4                    55241.622763  8.945334e+05  \n",
              "BILL_AMT5                    51258.875965  9.349555e+05  \n",
              "BILL_AMT6                    49784.324009  9.728818e+05  \n",
              "PAY_AMT1                      5458.940866  4.959326e+05  \n",
              "PAY_AMT2                      5485.238218  1.230354e+06  \n",
              "PAY_AMT3                      4750.194567  8.976219e+05  \n",
              "PAY_AMT4                      4359.049243  5.004725e+05  \n",
              "PAY_AMT5                      4382.996660  4.256687e+05  \n",
              "PAY_AMT6                      4383.299546  5.303265e+05  \n",
              "default.payment.next.month       0.000000  1.000000e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4866ad23-4f00-4c6c-b019-3794dace169a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LIMIT_BAL</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>167126.662762</td>\n",
              "      <td>129389.679875</td>\n",
              "      <td>-401.439185</td>\n",
              "      <td>55279.994116</td>\n",
              "      <td>137605.335989</td>\n",
              "      <td>238648.339988</td>\n",
              "      <td>9.935403e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEX</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>1.597532</td>\n",
              "      <td>0.490400</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EDUCATION</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>1.856117</td>\n",
              "      <td>0.795364</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MARRIAGE</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>1.556391</td>\n",
              "      <td>0.520025</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AGE</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>34.913598</td>\n",
              "      <td>9.213024</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>7.900000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_0</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>-0.010840</td>\n",
              "      <td>1.121737</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_2</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>-0.134203</td>\n",
              "      <td>1.196782</td>\n",
              "      <td>-3.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_3</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>-0.163703</td>\n",
              "      <td>1.195190</td>\n",
              "      <td>-3.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_4</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>-0.223964</td>\n",
              "      <td>1.153696</td>\n",
              "      <td>-3.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_5</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>-0.272605</td>\n",
              "      <td>1.118507</td>\n",
              "      <td>-3.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_6</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>-0.291966</td>\n",
              "      <td>1.136455</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT1</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>51120.824255</td>\n",
              "      <td>74036.985755</td>\n",
              "      <td>-34788.432550</td>\n",
              "      <td>5736.131465</td>\n",
              "      <td>23415.854687</td>\n",
              "      <td>66601.246049</td>\n",
              "      <td>9.603367e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT2</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>49160.619566</td>\n",
              "      <td>71578.361608</td>\n",
              "      <td>-47703.143243</td>\n",
              "      <td>5208.137901</td>\n",
              "      <td>22541.778530</td>\n",
              "      <td>64243.516380</td>\n",
              "      <td>9.829584e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT3</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>47086.858939</td>\n",
              "      <td>69229.258620</td>\n",
              "      <td>-153884.093383</td>\n",
              "      <td>4865.387200</td>\n",
              "      <td>21484.982297</td>\n",
              "      <td>60808.080841</td>\n",
              "      <td>8.570844e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT4</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>43350.776213</td>\n",
              "      <td>64852.723120</td>\n",
              "      <td>-192387.606577</td>\n",
              "      <td>4360.350182</td>\n",
              "      <td>19609.362333</td>\n",
              "      <td>55241.622763</td>\n",
              "      <td>8.945334e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT5</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>40357.187166</td>\n",
              "      <td>60887.223735</td>\n",
              "      <td>-77988.204777</td>\n",
              "      <td>3754.384863</td>\n",
              "      <td>18258.953502</td>\n",
              "      <td>51258.875965</td>\n",
              "      <td>9.349555e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT6</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>38990.324240</td>\n",
              "      <td>59592.733877</td>\n",
              "      <td>-233709.690512</td>\n",
              "      <td>3298.620791</td>\n",
              "      <td>17445.779665</td>\n",
              "      <td>49784.324009</td>\n",
              "      <td>9.728818e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT1</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>5681.427723</td>\n",
              "      <td>15529.563304</td>\n",
              "      <td>-8614.726686</td>\n",
              "      <td>816.994345</td>\n",
              "      <td>2433.939769</td>\n",
              "      <td>5458.940866</td>\n",
              "      <td>4.959326e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT2</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>5884.799531</td>\n",
              "      <td>20210.866543</td>\n",
              "      <td>-13809.309671</td>\n",
              "      <td>584.506525</td>\n",
              "      <td>2426.046817</td>\n",
              "      <td>5485.238218</td>\n",
              "      <td>1.230354e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT3</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>5315.574885</td>\n",
              "      <td>17667.839286</td>\n",
              "      <td>-8212.163893</td>\n",
              "      <td>464.847943</td>\n",
              "      <td>1951.106039</td>\n",
              "      <td>4750.194567</td>\n",
              "      <td>8.976219e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT4</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>4923.458746</td>\n",
              "      <td>16061.663860</td>\n",
              "      <td>-7785.048513</td>\n",
              "      <td>373.986983</td>\n",
              "      <td>1705.945428</td>\n",
              "      <td>4359.049243</td>\n",
              "      <td>5.004725e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT5</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>4866.373531</td>\n",
              "      <td>15400.026636</td>\n",
              "      <td>-7680.862564</td>\n",
              "      <td>370.304458</td>\n",
              "      <td>1752.391968</td>\n",
              "      <td>4382.996660</td>\n",
              "      <td>4.256687e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT6</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>5260.429843</td>\n",
              "      <td>18180.141450</td>\n",
              "      <td>-8498.549728</td>\n",
              "      <td>317.505486</td>\n",
              "      <td>1715.574673</td>\n",
              "      <td>4383.299546</td>\n",
              "      <td>5.303265e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default.payment.next.month</th>\n",
              "      <td>49999.0</td>\n",
              "      <td>0.218204</td>\n",
              "      <td>0.413031</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    </button>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
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              "      fill: #D2E3FC;\n",
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              "      background-color: #434B5C;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4866ad23-4f00-4c6c-b019-3794dace169a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "            document.querySelector('#df-346226ca-f5d3-4750-a9aa-96130df75e75 button');\n",
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              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 24,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 49999.0,\n        \"max\": 49999.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          49999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36834.60696872685,\n        \"min\": -0.2919658393167863,\n        \"max\": 167126.66276225916,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          -0.2239644792895858\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35333.839998387586,\n        \"min\": 0.4130310297560564,\n        \"max\": 129389.67987466037,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          1.1536958679759035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65621.10367217154,\n        \"min\": -233709.69051218065,\n        \"max\": 18.0,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          -401.4391850176344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11191.784634285203,\n        \"min\": -1.0,\n        \"max\": 55279.9941163649,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          55279.9941163649\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28290.924426732152,\n        \"min\": 0.0,\n        \"max\": 137605.33598896593,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          137605.33598896593\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51805.86248230135,\n        \"min\": 0.0,\n        \"max\": 238648.33998769178,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          238648.33998769178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 454905.72701625945,\n        \"min\": 1.0,\n        \"max\": 1230354.176656892,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          993540.2615185528\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Target Class Distribution"
      ],
      "metadata": {
        "id": "Oqdj98i5UGWx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "sns.countplot(data=df, x='default.payment.next.month', palette='coolwarm')\n",
        "plt.title('Target Distribution: Default Next Month')\n",
        "plt.xticks([0, 1], ['No Default', 'Default'])\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "tsYX9_iBT5qG",
        "outputId": "564d1a98-96e0-4825-a2f9-5206550ac2d5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-3685336274.py:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(data=df, x='default.payment.next.month', palette='coolwarm')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation Heatmap"
      ],
      "metadata": {
        "id": "klgxiPQ8ULH7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(14, 10))\n",
        "sns.heatmap(df.corr(), cmap='coolwarm', annot=False)\n",
        "plt.title(\"Feature Correlation Matrix\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OzqZEmGZUJe7",
        "outputId": "bcf7292d-e53b-439e-c3b0-8db7a93106f1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distribution Plots of Key Features"
      ],
      "metadata": {
        "id": "q6OTjsbJUTWJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "sns.histplot(data=df, x='LIMIT_BAL', hue='default.payment.next.month', kde=True, element=\"step\")\n",
        "plt.title('Limit Balance Distribution by Default')\n",
        "plt.show()\n",
        "\n",
        "sns.histplot(data=df, x='AGE', hue='default.payment.next.month', kde=True, element=\"step\")\n",
        "plt.title('Age Distribution by Default')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "e4PH6oHoUNJL",
        "outputId": "3d992367-d3d7-4caf-95c6-78c8d055061d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Boxplots for Pay History vs Default"
      ],
      "metadata": {
        "id": "kAgkIDpjUWRU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "pay_cols = [f'PAY_{i}' for i in [0,2,3,4,5,6]]\n",
        "\n",
        "plt.figure(figsize=(15, 6))\n",
        "df_melt = pd.melt(df, id_vars='default.payment.next.month', value_vars=pay_cols)\n",
        "sns.boxplot(data=df_melt, x='variable', y='value', hue='default.payment.next.month')\n",
        "plt.title('Payment Delay History by Default Status')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "qWG_mnr7UV3N",
        "outputId": "deb477bb-3634-40ce-c1af-7935defcfda1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "mIsXkVvhUfmb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train High-Accuracy Model with AutoGluon\n",
        "from autogluon.tabular import TabularPredictor\n",
        "\n",
        "predictor = TabularPredictor(label='default.payment.next.month', eval_metric='accuracy')\n",
        "predictor.fit(df, time_limit=600)  # train for max 10 minutes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNR7NzMMUbma",
        "outputId": "b8e7f5f3-d6d4-405d-cc18-7710d17c751d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20250731_115203\"\n",
            "Verbosity: 2 (Standard Logging)\n",
            "=================== System Info ===================\n",
            "AutoGluon Version:  1.4.0\n",
            "Python Version:     3.11.13\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025\n",
            "CPU Count:          2\n",
            "Memory Avail:       11.09 GB / 12.67 GB (87.5%)\n",
            "Disk Space Avail:   64.51 GB / 107.72 GB (59.9%)\n",
            "===================================================\n",
            "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n",
            "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n",
            "\tpresets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.\n",
            "\tpresets='best'    : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n",
            "\tpresets='high'    : Strong accuracy with fast inference speed.\n",
            "\tpresets='good'    : Good accuracy with very fast inference speed.\n",
            "\tpresets='medium'  : Fast training time, ideal for initial prototyping.\n",
            "Using hyperparameters preset: hyperparameters='default'\n",
            "Beginning AutoGluon training ... Time limit = 600s\n",
            "AutoGluon will save models to \"/content/AutogluonModels/ag-20250731_115203\"\n",
            "Train Data Rows:    49999\n",
            "Train Data Columns: 23\n",
            "Label Column:       default.payment.next.month\n",
            "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
            "\t2 unique label values:  [np.int64(0), np.int64(1)]\n",
            "\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n",
            "Problem Type:       binary\n",
            "Preprocessing data ...\n",
            "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    11362.79 MB\n",
            "\tTrain Data (Original)  Memory Usage: 8.77 MB (0.1% of available memory)\n",
            "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
            "\tStage 1 Generators:\n",
            "\t\tFitting AsTypeFeatureGenerator...\n",
            "\t\t\tNote: Converting 1 features to boolean dtype as they only contain 2 unique values.\n",
            "\tStage 2 Generators:\n",
            "\t\tFitting FillNaFeatureGenerator...\n",
            "\tStage 3 Generators:\n",
            "\t\tFitting IdentityFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tStage 5 Generators:\n",
            "\t\tFitting DropDuplicatesFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('float', []) : 13 | ['LIMIT_BAL', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', ...]\n",
            "\t\t('int', [])   : 10 | ['SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', ...]\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('float', [])     : 13 | ['LIMIT_BAL', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', ...]\n",
            "\t\t('int', [])       :  9 | ['EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', ...]\n",
            "\t\t('int', ['bool']) :  1 | ['SEX']\n",
            "\t0.2s = Fit runtime\n",
            "\t23 features in original data used to generate 23 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 8.44 MB (0.1% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.25s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "Automatically generating train/validation split with holdout_frac=0.0500010000200004, Train Rows: 47499, Val Rows: 2500\n",
            "User-specified model hyperparameters to be fit:\n",
            "{\n",
            "\t'NN_TORCH': [{}],\n",
            "\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}],\n",
            "\t'CAT': [{}],\n",
            "\t'XGB': [{}],\n",
            "\t'FASTAI': [{}],\n",
            "\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "}\n",
            "Fitting 11 L1 models, fit_strategy=\"sequential\" ...\n",
            "Fitting model: LightGBMXT ... Training model for up to 599.75s of the 599.75s of remaining time.\n",
            "\tFitting with cpus=1, gpus=0, mem=0.1/10.9 GB\n",
            "\t0.826\t = Validation score   (accuracy)\n",
            "\t12.18s\t = Training   runtime\n",
            "\t0.04s\t = Validation runtime\n",
            "Fitting model: LightGBM ... Training model for up to 587.52s of the 587.51s of remaining time.\n",
            "\tFitting with cpus=1, gpus=0, mem=0.1/10.8 GB\n",
            "\t0.8372\t = Validation score   (accuracy)\n",
            "\t3.06s\t = Training   runtime\n",
            "\t0.04s\t = Validation runtime\n",
            "Fitting model: RandomForestGini ... Training model for up to 584.38s of the 584.38s of remaining time.\n",
            "\tFitting with cpus=2, gpus=0, mem=0.0/10.8 GB\n",
            "\t0.8704\t = Validation score   (accuracy)\n",
            "\t72.84s\t = Training   runtime\n",
            "\t0.23s\t = Validation runtime\n",
            "Fitting model: RandomForestEntr ... Training model for up to 510.71s of the 510.70s of remaining time.\n",
            "\tFitting with cpus=2, gpus=0, mem=0.0/10.8 GB\n",
            "\t0.866\t = Validation score   (accuracy)\n",
            "\t82.21s\t = Training   runtime\n",
            "\t0.23s\t = Validation runtime\n",
            "Fitting model: CatBoost ... Training model for up to 427.73s of the 427.72s of remaining time.\n",
            "\tFitting with cpus=1, gpus=0\n",
            "\t0.844\t = Validation score   (accuracy)\n",
            "\t43.87s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: ExtraTreesGini ... Training model for up to 383.83s of the 383.83s of remaining time.\n",
            "\tFitting with cpus=2, gpus=0, mem=0.0/10.9 GB\n",
            "\t0.9016\t = Validation score   (accuracy)\n",
            "\t14.47s\t = Training   runtime\n",
            "\t0.51s\t = Validation runtime\n",
            "Fitting model: ExtraTreesEntr ... Training model for up to 367.61s of the 367.61s of remaining time.\n",
            "\tFitting with cpus=2, gpus=0, mem=0.0/10.8 GB\n",
            "\t0.8992\t = Validation score   (accuracy)\n",
            "\t14.49s\t = Training   runtime\n",
            "\t0.27s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI ... Training model for up to 351.76s of the 351.76s of remaining time.\n",
            "\tFitting with cpus=1, gpus=0, mem=0.1/10.6 GB\n",
            "\t0.8552\t = Validation score   (accuracy)\n",
            "\t57.36s\t = Training   runtime\n",
            "\t0.07s\t = Validation runtime\n",
            "Fitting model: XGBoost ... Training model for up to 294.25s of the 294.25s of remaining time.\n",
            "\tFitting with cpus=1, gpus=0\n",
            "\t0.8504\t = Validation score   (accuracy)\n",
            "\t8.13s\t = Training   runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: NeuralNetTorch ... Training model for up to 286.02s of the 286.02s of remaining time.\n",
            "\tFitting with cpus=1, gpus=0, mem=0.0/10.7 GB\n",
            "\tRan out of time, stopping training early. (Stopping on epoch 182)\n",
            "\t0.8712\t = Validation score   (accuracy)\n",
            "\t285.73s\t = Training   runtime\n",
            "\t0.05s\t = Validation runtime\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.00s of the 0.21s of remaining time.\n",
            "\tEnsemble Weights: {'ExtraTreesGini': 1.0}\n",
            "\t0.9016\t = Validation score   (accuracy)\n",
            "\t0.24s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 600.09s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 4890.0 rows/s (2500 batch size)\n",
            "Disabling decision threshold calibration for metric `accuracy` due to having fewer than 10000 rows of validation data for calibration, to avoid overfitting (2500 rows).\n",
            "\t`accuracy` is generally not improved through threshold calibration. Force calibration via specifying `calibrate_decision_threshold=True`.\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/content/AutogluonModels/ag-20250731_115203\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<autogluon.tabular.predictor.predictor.TabularPredictor at 0x7fad82de9950>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Eval"
      ],
      "metadata": {
        "id": "bdna_iwlUlWg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Title: Evaluate Model Accuracy & Metrics\n",
        "performance = predictor.evaluate(df)"
      ],
      "metadata": {
        "id": "Nv5Np_JOUnZs"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Title: Visualize Top Feature Importances\n",
        "importance_df = predictor.feature_importance(df)\n",
        "top_feats = importance_df.head(10).sort_values(\"importance\")\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.barplot(x=top_feats[\"importance\"], y=top_feats.index, palette=\"viridis\")\n",
        "plt.title(\"Top 10 Important Features\")\n",
        "plt.xlabel(\"Importance Score\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "mMIK7DP5UoZK",
        "outputId": "c550767a-6f3f-471c-aecd-506c1d75ee58"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Computing feature importance via permutation shuffling for 23 features using 5000 rows with 5 shuffle sets...\n",
            "\t271.5s\t= Expected runtime (54.3s per shuffle set)\n",
            "\t49.97s\t= Actual runtime (Completed 5 of 5 shuffle sets)\n",
            "/tmp/ipython-input-3299372863.py:6: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=top_feats[\"importance\"], y=top_feats.index, palette=\"viridis\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S12QJIzlXtJi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}